activity
20172025
most citedBridging Bayesian, frequentist and fiducial (BFF) inferences using confidence distribution

5 citations · 9 across the 7 of their papers we have counts for

collaborators

7 papers

physics.atom-ph2025

Atomic clock frequency ratios with fractional uncertainty

Alexander Aeppli, Willa J. Arthur-Dworschack, Kyle Beloy +24

We report high-precision frequency ratio measurements between optical atomic clocks based on Al, Yb, and Sr. With total fractional uncertainties at or bel…

stat.AP2024

Ethical considerations for data involving human gender and sex variables

Suzanne Thornton, Rochelle E. Tractenberg

The inclusion of human sex and gender data in statistical analysis invokes multiple considerations for data collection, combination, analysis, and interpretation. These considerati…

stat.OT2023★ 3 cited

The Mastery Rubric for Statistics and Data Science: promoting coherence and consistency in data science education and training

Rochelle E. Tractenberg, Donna LaLonde, Suzanne Thornton

Consensus based publications of both competencies and undergraduate curriculum guidance documents targeting data science instruction for higher education have recently been publish…

stat.ME2022

Approximate confidence distribution computing

Suzanne Thornton, Wentao Li, Minge Xie

Approximate confidence distribution computing (ACDC) offers a new take on the rapidly developing field of likelihood-free inference from within a frequentist framework. The appeal…

stat.ME2021

Best Practices for Collecting Gender and Sex Data

Suzanne Thornton, Dooti Roy, Stephen Parry +4

The measurement and analysis of human sex and gender is a nuanced problem with many overlapping considerations including statistical bias, data privacy, and the ethical treatment o…

stat.ME2020★ 5 cited

Bridging Bayesian, frequentist and fiducial (BFF) inferences using confidence distribution

Suzanne Thornton, Minge Xie

Bayesian, frequentist and fiducial (BFF) inferences are much more congruous than they have been perceived historically in the scientific community (cf., Reid and Cox 2015; Kass 201…

stat.CO2017★ 1 cited

An effective likelihood-free approximate computing method with statistical inferential guarantees

Suzanne Thornton, Wentao Li, Min-ge Xie

Approximate Bayesian computing is a powerful likelihood-free method that has grown increasingly popular since early applications in population genetics. However, complications aris…